# -*- coding: utf-8 -*-
from datetime import timedelta
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from airflow.exceptions import AirflowSkipException
from jms_hi.dm.dm_hour_out_collect_detail_hi import jms_dm__dm_hour_out_collect_detail_hi
import pendulum
cst = pendulum.timezone('Asia/Shanghai')

class HiSparksqlOperator(SparkSqlOperator):
    def pre_execute(self, context):
        day = cst.convert(context['ti'].execution_date) + timedelta(days=1)

        days_of_hours = ['07']

        if day.strftime('%H') not in days_of_hours:
            print(f'{day.strftime("%H")} not in {days_of_hours}, should skip')
            raise AirflowSkipException()
        else:
            print(f'{day.strftime("%H")} in {days_of_hours}, run now')
            super().pre_execute(context)

jms_dm__dm_hour_out_collect_sum_hi = HiSparksqlOperator(
    task_id='jms_dm__dm_hour_out_collect_sum_hi',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    execution_timeout=timedelta(hours=1),
    depends_on_past=True,
    email=['matthew.xiong@jtexpress.com','yl_bigdata@yl-scm.com'],
    name='jms_dm__dm_hour_out_collect_sum_hi_{{ execution_date |hour_add(1)| cst_hour }}',
    sql='jms_hi/dm/dm_hour_out_collect_sum_hi/execute.sql',
    executor_cores=1,
    executor_memory='2G',
    num_executors=2,
    conf={
        'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
        'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
        'spark.dynamicAllocation.maxExecutors': 3,  # 动态资源最大扩容 Executor 数
        'spark.dynamicAllocation.cachedExecutorIdleTimeout': 120,  # 动态资源自动释放闲置 Executor 的超时时间(s)
        'spark.executor.memoryOverhead': '1G',  # 堆外内存
        'spark.hadoop.hive.exec.dynamic.partition.mode': 'nonstrict', # 动态分区
        'spark.hadoop.hive.exec.dynamic.partition': 'true',
        'spark.sql.shuffle.partitions' : 50
    },
    yarn_queue='pro',
)

jms_dm__dm_hour_out_collect_sum_hi << [
    jms_dm__dm_hour_out_collect_detail_hi
]
